Overview

Dataset statistics

Number of variables16
Number of observations775
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory92.3 KiB
Average record size in memory122.0 B

Variable types

Categorical7
Numeric6
Boolean3

Alerts

adult_male is highly overall correlated with alive and 3 other fieldsHigh correlation
age is highly overall correlated with age_standardized and 1 other fieldsHigh correlation
age_standardized is highly overall correlated with age and 1 other fieldsHigh correlation
alive is highly overall correlated with adult_male and 3 other fieldsHigh correlation
alone is highly overall correlated with parch and 1 other fieldsHigh correlation
class is highly overall correlated with pclassHigh correlation
embark_town is highly overall correlated with embarkedHigh correlation
embarked is highly overall correlated with embark_townHigh correlation
fare is highly overall correlated with fare_normalizedHigh correlation
fare_normalized is highly overall correlated with fareHigh correlation
parch is highly overall correlated with aloneHigh correlation
pclass is highly overall correlated with classHigh correlation
sex is highly overall correlated with adult_male and 3 other fieldsHigh correlation
sibsp is highly overall correlated with aloneHigh correlation
survived is highly overall correlated with adult_male and 3 other fieldsHigh correlation
who is highly overall correlated with adult_male and 5 other fieldsHigh correlation
sibsp has 508 (65.5%) zerosZeros
parch has 571 (73.7%) zerosZeros
fare has 9 (1.2%) zerosZeros
fare_normalized has 9 (1.2%) zerosZeros

Reproduction

Analysis started2025-12-14 01:34:11.304089
Analysis finished2025-12-14 01:34:12.826792
Duration1.52 second
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

survived
Categorical

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
0
455 
1
320 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters775
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0455
58.7%
1320
41.3%

Length

2025-12-13T17:34:12.848823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-13T17:34:12.870849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0455
58.7%
1320
41.3%

Most occurring characters

ValueCountFrequency (%)
0455
58.7%
1320
41.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)775
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0455
58.7%
1320
41.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)775
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0455
58.7%
1320
41.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)775
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0455
58.7%
1320
41.3%

pclass
Categorical

High correlation 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
3
401 
1
210 
2
164 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters775
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
3401
51.7%
1210
27.1%
2164
21.2%

Length

2025-12-13T17:34:12.891331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-13T17:34:12.907486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3401
51.7%
1210
27.1%
2164
21.2%

Most occurring characters

ValueCountFrequency (%)
3401
51.7%
1210
27.1%
2164
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)775
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3401
51.7%
1210
27.1%
2164
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)775
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3401
51.7%
1210
27.1%
2164
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)775
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3401
51.7%
1210
27.1%
2164
21.2%

sex
Categorical

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
male
483 
female
292 

Length

Max length6
Median length4
Mean length4.7535484
Min length4

Characters and Unicode

Total characters3684
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowfemale
4th rowfemale
5th rowmale

Common Values

ValueCountFrequency (%)
male483
62.3%
female292
37.7%

Length

2025-12-13T17:34:12.928051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-13T17:34:12.943694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male483
62.3%
female292
37.7%

Most occurring characters

ValueCountFrequency (%)
e1067
29.0%
m775
21.0%
a775
21.0%
l775
21.0%
f292
 
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)3684
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1067
29.0%
m775
21.0%
a775
21.0%
l775
21.0%
f292
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3684
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1067
29.0%
m775
21.0%
a775
21.0%
l775
21.0%
f292
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3684
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1067
29.0%
m775
21.0%
a775
21.0%
l775
21.0%
f292
 
7.9%

age
Real number (ℝ)

High correlation 

Distinct88
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.581187
Minimum0.42
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2025-12-13T17:34:12.965413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile4.7
Q121
median28
Q336
95-th percentile55.15
Maximum80
Range79.58
Interquartile range (IQR)15

Descriptive statistics

Standard deviation13.766359
Coefficient of variation (CV)0.46537546
Kurtosis0.56732321
Mean29.581187
Median Absolute Deviation (MAD)7
Skewness0.44198678
Sum22925.42
Variance189.51263
MonotonicityNot monotonic
2025-12-13T17:34:12.996945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28121
 
15.6%
2429
 
3.7%
1825
 
3.2%
2224
 
3.1%
1923
 
3.0%
2122
 
2.8%
3022
 
2.8%
3621
 
2.7%
2520
 
2.6%
2919
 
2.5%
Other values (78)449
57.9%
ValueCountFrequency (%)
0.421
 
0.1%
0.671
 
0.1%
0.751
 
0.1%
0.832
 
0.3%
0.921
 
0.1%
17
0.9%
210
1.3%
36
0.8%
410
1.3%
54
 
0.5%
ValueCountFrequency (%)
801
 
0.1%
741
 
0.1%
712
0.3%
70.51
 
0.1%
702
0.3%
661
 
0.1%
653
0.4%
642
0.3%
632
0.3%
623
0.4%

sibsp
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52903226
Minimum0
Maximum8
Zeros508
Zeros (%)65.5%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2025-12-13T17:34:13.020405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2.3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9903258
Coefficient of variation (CV)1.8719573
Kurtosis12.608666
Mean0.52903226
Median Absolute Deviation (MAD)0
Skewness3.0360781
Sum410
Variance0.98074519
MonotonicityNot monotonic
2025-12-13T17:34:13.040187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0508
65.5%
1201
 
25.9%
227
 
3.5%
418
 
2.3%
314
 
1.8%
55
 
0.6%
82
 
0.3%
ValueCountFrequency (%)
0508
65.5%
1201
 
25.9%
227
 
3.5%
314
 
1.8%
418
 
2.3%
55
 
0.6%
82
 
0.3%
ValueCountFrequency (%)
82
 
0.3%
55
 
0.6%
418
 
2.3%
314
 
1.8%
227
 
3.5%
1201
 
25.9%
0508
65.5%

parch
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.42064516
Minimum0
Maximum6
Zeros571
Zeros (%)73.7%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2025-12-13T17:34:13.058143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.84056527
Coefficient of variation (CV)1.9982763
Kurtosis8.8375634
Mean0.42064516
Median Absolute Deviation (MAD)0
Skewness2.6133475
Sum326
Variance0.70654997
MonotonicityNot monotonic
2025-12-13T17:34:13.076118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0571
73.7%
1114
 
14.7%
275
 
9.7%
55
 
0.6%
35
 
0.6%
44
 
0.5%
61
 
0.1%
ValueCountFrequency (%)
0571
73.7%
1114
 
14.7%
275
 
9.7%
35
 
0.6%
44
 
0.5%
55
 
0.6%
61
 
0.1%
ValueCountFrequency (%)
61
 
0.1%
55
 
0.6%
44
 
0.5%
35
 
0.6%
275
 
9.7%
1114
 
14.7%
0571
73.7%

fare
Real number (ℝ)

High correlation  Zeros 

Distinct248
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.878403
Minimum0
Maximum512.3292
Zeros9
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2025-12-13T17:34:13.135088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.225
Q18.05
median15.9
Q334.1979
95-th percentile120
Maximum512.3292
Range512.3292
Interquartile range (IQR)26.1479

Descriptive statistics

Standard deviation52.408474
Coefficient of variation (CV)1.5026053
Kurtosis29.905898
Mean34.878403
Median Absolute Deviation (MAD)8.3792
Skewness4.5499504
Sum27030.762
Variance2746.6481
MonotonicityNot monotonic
2025-12-13T17:34:13.164344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1331
 
4.0%
2630
 
3.9%
8.0525
 
3.2%
10.523
 
3.0%
7.7520
 
2.6%
7.895819
 
2.5%
7.92516
 
2.1%
7.77516
 
2.1%
26.5513
 
1.7%
7.854212
 
1.5%
Other values (238)570
73.5%
ValueCountFrequency (%)
09
1.2%
4.01251
 
0.1%
51
 
0.1%
6.23751
 
0.1%
6.43751
 
0.1%
6.451
 
0.1%
6.49582
 
0.3%
6.752
 
0.3%
6.85831
 
0.1%
6.951
 
0.1%
ValueCountFrequency (%)
512.32923
0.4%
2634
0.5%
262.3752
0.3%
247.52082
0.3%
227.5254
0.5%
221.77921
 
0.1%
211.51
 
0.1%
211.33753
0.4%
164.86672
0.3%
153.46253
0.4%

embarked
Categorical

High correlation 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
S
562 
C
155 
Q
58 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters775
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowC
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S562
72.5%
C155
 
20.0%
Q58
 
7.5%

Length

2025-12-13T17:34:13.190763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-13T17:34:13.206168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
s562
72.5%
c155
 
20.0%
q58
 
7.5%

Most occurring characters

ValueCountFrequency (%)
S562
72.5%
C155
 
20.0%
Q58
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)775
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S562
72.5%
C155
 
20.0%
Q58
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)775
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S562
72.5%
C155
 
20.0%
Q58
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)775
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S562
72.5%
C155
 
20.0%
Q58
 
7.5%

class
Categorical

High correlation 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
Third
401 
First
210 
Second
164 

Length

Max length6
Median length5
Mean length5.2116129
Min length5

Characters and Unicode

Total characters4039
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThird
2nd rowFirst
3rd rowThird
4th rowFirst
5th rowThird

Common Values

ValueCountFrequency (%)
Third401
51.7%
First210
27.1%
Second164
21.2%

Length

2025-12-13T17:34:13.225426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-13T17:34:13.241038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
third401
51.7%
first210
27.1%
second164
21.2%

Most occurring characters

ValueCountFrequency (%)
i611
15.1%
r611
15.1%
d565
14.0%
T401
9.9%
h401
9.9%
F210
 
5.2%
s210
 
5.2%
t210
 
5.2%
S164
 
4.1%
e164
 
4.1%
Other values (3)492
12.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)4039
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i611
15.1%
r611
15.1%
d565
14.0%
T401
9.9%
h401
9.9%
F210
 
5.2%
s210
 
5.2%
t210
 
5.2%
S164
 
4.1%
e164
 
4.1%
Other values (3)492
12.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4039
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i611
15.1%
r611
15.1%
d565
14.0%
T401
9.9%
h401
9.9%
F210
 
5.2%
s210
 
5.2%
t210
 
5.2%
S164
 
4.1%
e164
 
4.1%
Other values (3)492
12.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4039
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i611
15.1%
r611
15.1%
d565
14.0%
T401
9.9%
h401
9.9%
F210
 
5.2%
s210
 
5.2%
t210
 
5.2%
S164
 
4.1%
e164
 
4.1%
Other values (3)492
12.2%

who
Categorical

High correlation 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
man
443 
woman
250 
child
82 

Length

Max length5
Median length3
Mean length3.8567742
Min length3

Characters and Unicode

Total characters2989
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowman
2nd rowwoman
3rd rowwoman
4th rowwoman
5th rowman

Common Values

ValueCountFrequency (%)
man443
57.2%
woman250
32.3%
child82
 
10.6%

Length

2025-12-13T17:34:13.261978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-13T17:34:13.277588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
man443
57.2%
woman250
32.3%
child82
 
10.6%

Most occurring characters

ValueCountFrequency (%)
m693
23.2%
a693
23.2%
n693
23.2%
w250
 
8.4%
o250
 
8.4%
c82
 
2.7%
h82
 
2.7%
i82
 
2.7%
l82
 
2.7%
d82
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)2989
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m693
23.2%
a693
23.2%
n693
23.2%
w250
 
8.4%
o250
 
8.4%
c82
 
2.7%
h82
 
2.7%
i82
 
2.7%
l82
 
2.7%
d82
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2989
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m693
23.2%
a693
23.2%
n693
23.2%
w250
 
8.4%
o250
 
8.4%
c82
 
2.7%
h82
 
2.7%
i82
 
2.7%
l82
 
2.7%
d82
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2989
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m693
23.2%
a693
23.2%
n693
23.2%
w250
 
8.4%
o250
 
8.4%
c82
 
2.7%
h82
 
2.7%
i82
 
2.7%
l82
 
2.7%
d82
 
2.7%

adult_male
Boolean

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
True
443 
False
332 
ValueCountFrequency (%)
True443
57.2%
False332
42.8%
2025-12-13T17:34:13.289730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

embark_town
Categorical

High correlation 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
Southampton
562 
Cherbourg
155 
Queenstown
58 

Length

Max length11
Median length11
Mean length10.525161
Min length9

Characters and Unicode

Total characters8157
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouthampton
2nd rowCherbourg
3rd rowSouthampton
4th rowSouthampton
5th rowSouthampton

Common Values

ValueCountFrequency (%)
Southampton562
72.5%
Cherbourg155
 
20.0%
Queenstown58
 
7.5%

Length

2025-12-13T17:34:13.309436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-13T17:34:13.325585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
southampton562
72.5%
cherbourg155
 
20.0%
queenstown58
 
7.5%

Most occurring characters

ValueCountFrequency (%)
o1337
16.4%
t1182
14.5%
u775
9.5%
h717
8.8%
n678
8.3%
p562
6.9%
S562
6.9%
m562
6.9%
a562
6.9%
r310
 
3.8%
Other values (7)910
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)8157
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o1337
16.4%
t1182
14.5%
u775
9.5%
h717
8.8%
n678
8.3%
p562
6.9%
S562
6.9%
m562
6.9%
a562
6.9%
r310
 
3.8%
Other values (7)910
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8157
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o1337
16.4%
t1182
14.5%
u775
9.5%
h717
8.8%
n678
8.3%
p562
6.9%
S562
6.9%
m562
6.9%
a562
6.9%
r310
 
3.8%
Other values (7)910
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8157
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o1337
16.4%
t1182
14.5%
u775
9.5%
h717
8.8%
n678
8.3%
p562
6.9%
S562
6.9%
m562
6.9%
a562
6.9%
r310
 
3.8%
Other values (7)910
11.2%

alive
Boolean

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
False
455 
True
320 
ValueCountFrequency (%)
False455
58.7%
True320
41.3%
2025-12-13T17:34:13.337977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

alone
Boolean

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
True
437 
False
338 
ValueCountFrequency (%)
True437
56.4%
False338
43.6%
2025-12-13T17:34:13.349228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

fare_normalized
Real number (ℝ)

High correlation  Zeros 

Distinct248
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.068078109
Minimum0
Maximum1
Zeros9
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2025-12-13T17:34:13.370242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.014102261
Q10.015712554
median0.031034733
Q30.066749855
95-th percentile0.2342244
Maximum1
Range1
Interquartile range (IQR)0.051037302

Descriptive statistics

Standard deviation0.10229453
Coefficient of variation (CV)1.5026053
Kurtosis29.905898
Mean0.068078109
Median Absolute Deviation (MAD)0.016355109
Skewness4.5499504
Sum52.760534
Variance0.01046417
MonotonicityNot monotonic
2025-12-13T17:34:13.400122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0253743101131
 
4.0%
0.0507486202230
 
3.9%
0.0157125535725
 
3.2%
0.0204946350923
 
3.0%
0.0151269925720
 
2.6%
0.0154115752119
 
2.5%
0.0154685698216
 
2.1%
0.0151757893216
 
2.1%
0.0518221487313
 
1.7%
0.0153303774212
 
1.5%
Other values (238)570
73.5%
ValueCountFrequency (%)
09
1.2%
0.0078318784091
 
0.1%
0.0097593500431
 
0.1%
0.012174789181
 
0.1%
0.012565163181
 
0.1%
0.012589561561
 
0.1%
0.01267895722
 
0.3%
0.013175122562
 
0.3%
0.013386510081
 
0.1%
0.013565496561
 
0.1%
ValueCountFrequency (%)
13
0.4%
0.51334181234
0.5%
0.51212189352
0.3%
0.4831284262
0.3%
0.44409922374
0.5%
0.4328841691
 
0.1%
0.41282050681
 
0.1%
0.41250332793
0.4%
0.32179836712
0.3%
0.29953885123
0.4%

age_standardized
Real number (ℝ)

High correlation 

Distinct88
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3837563 × 10-16
Minimum-2.1196614
Maximum3.6648306
Zeros0
Zeros (%)0.0%
Negative459
Negative (%)59.2%
Memory size12.1 KiB
2025-12-13T17:34:13.427570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2.1196614
5-th percentile-1.8085578
Q1-0.62374728
median-0.11493295
Q30.46656914
95-th percentile1.8585398
Maximum3.6648306
Range5.7844921
Interquartile range (IQR)1.0903164

Descriptive statistics

Standard deviation1.0006458
Coefficient of variation (CV)4.1977689 × 1015
Kurtosis0.56732321
Mean2.3837563 × 10-16
Median Absolute Deviation (MAD)0.50881433
Skewness0.44198678
Sum1.9184654 × 10-13
Variance1.001292
MonotonicityNot monotonic
2025-12-13T17:34:13.456315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1149329506121
 
15.6%
-0.405683996529
 
3.7%
-0.841810565425
 
3.2%
-0.551059519524
 
3.1%
-0.769122803923
 
3.0%
-0.62374728122
 
2.8%
0.0304425724222
 
2.8%
0.466569141321
 
2.7%
-0.33299623520
 
2.6%
-0.0422451890719
 
2.5%
Other values (78)449
57.9%
ValueCountFrequency (%)
-2.1196614121
 
0.1%
-2.1014894721
 
0.1%
-2.0956744511
 
0.1%
-2.089859432
 
0.3%
-2.0833175321
 
0.1%
-2.0775025117
0.9%
-2.00481474910
1.3%
-1.9321269886
0.8%
-1.85943922610
1.3%
-1.7867514654
 
0.5%
ValueCountFrequency (%)
3.6648306471
 
0.1%
3.2287040781
 
0.1%
3.0106407932
0.3%
2.9742969131
 
0.1%
2.9379530322
0.3%
2.6472019861
 
0.1%
2.5745142243
0.4%
2.5018264632
0.3%
2.4291387022
0.3%
2.356450943
0.4%

Interactions

2025-12-13T17:34:12.429880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:11.684886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:11.849674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:11.992522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:12.128575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:12.292861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:12.453587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:11.724488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:11.874723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:12.016404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:12.151601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:12.316162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:12.477461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:11.753785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:11.898685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:12.039597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:12.205452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:12.340172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:12.499304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:11.778886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:11.922400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:12.061712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:12.227069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:12.362439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:12.521585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:11.802425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:11.945116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:12.083742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:12.248680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:12.385319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:12.544299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:11.825987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:11.969664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:12.106830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:12.270673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T17:34:12.407199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-13T17:34:13.482961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
adult_maleageage_standardizedalivealoneclassembark_townembarkedfarefare_normalizedparchpclasssexsibspsurvivedwho
adult_male1.0000.3650.3650.5260.3810.0760.0710.0710.1490.1490.3880.0760.8950.2990.5260.999
age0.3651.0001.0000.1370.3300.2500.1370.1370.1260.126-0.2370.2500.083-0.1690.1370.650
age_standardized0.3651.0001.0000.1370.3300.2500.1370.1370.1260.126-0.2370.2500.083-0.1690.1370.650
alive0.5260.1370.1371.0000.1700.3310.1630.1630.2840.2840.1460.3310.5120.1540.9970.536
alone0.3810.3300.3300.1701.0000.1050.1000.1000.2870.2870.6740.1050.2720.8200.1700.433
class0.0760.2500.2500.3310.1051.0000.2520.2520.4960.4960.0291.0000.1180.1420.3310.144
embark_town0.0710.1370.1370.1630.1000.2521.0001.0000.1980.1980.0110.2520.0860.0920.1630.049
embarked0.0710.1370.1370.1630.1000.2521.0001.0000.1980.1980.0110.2520.0860.0920.1630.049
fare0.1490.1260.1260.2840.2870.4960.1980.1981.0001.0000.3800.4960.1810.4120.2840.158
fare_normalized0.1490.1260.1260.2840.2870.4960.1980.1981.0001.0000.3800.4960.1810.4120.2840.158
parch0.388-0.237-0.2370.1460.6740.0290.0110.0110.3800.3801.0000.0290.2320.4130.1460.385
pclass0.0760.2500.2500.3310.1051.0000.2520.2520.4960.4960.0291.0000.1180.1420.3310.144
sex0.8950.0830.0830.5120.2720.1180.0860.0860.1810.1810.2320.1181.0000.1690.5120.941
sibsp0.299-0.169-0.1690.1540.8200.1420.0920.0920.4120.4120.4130.1420.1691.0000.1540.364
survived0.5260.1370.1370.9970.1700.3310.1630.1630.2840.2840.1460.3310.5120.1541.0000.536
who0.9990.6500.6500.5360.4330.1440.0490.0490.1580.1580.3850.1440.9410.3640.5361.000

Missing values

2025-12-13T17:34:12.582781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-13T17:34:12.615878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

survivedpclasssexagesibspparchfareembarkedclasswhoadult_maleembark_townalivealonefare_normalizedage_standardized
003male22.0107.2500SThirdmanTrueSouthamptonnoFalse0.014151-0.551060
111female38.01071.2833CFirstwomanFalseCherbourgyesFalse0.1391360.611945
213female26.0007.9250SThirdwomanFalseSouthamptonyesTrue0.015469-0.260308
311female35.01053.1000SFirstwomanFalseSouthamptonyesFalse0.1036440.393881
403male35.0008.0500SThirdmanTrueSouthamptonnoTrue0.0157130.393881
503male28.0008.4583QThirdmanTrueQueenstownnoTrue0.016510-0.114933
601male54.00051.8625SFirstmanTrueSouthamptonnoTrue0.1012291.774949
703male2.03121.0750SThirdchildFalseSouthamptonnoFalse0.041136-2.004815
813female27.00211.1333SThirdwomanFalseSouthamptonyesFalse0.021731-0.187621
912female14.01030.0708CSecondchildFalseCherbourgyesFalse0.058694-1.132562
survivedpclasssexagesibspparchfareembarkedclasswhoadult_maleembark_townalivealonefare_normalizedage_standardized
87911female56.00183.1583CFirstwomanFalseCherbourgyesFalse0.1623141.920324
88012female25.00126.0000SSecondwomanFalseSouthamptonyesFalse0.050749-0.332996
88103male33.0007.8958SThirdmanTrueSouthamptonnoTrue0.0154120.248506
88203female22.00010.5167SThirdwomanFalseSouthamptonnoTrue0.020527-0.551060
88302male28.00010.5000SSecondmanTrueSouthamptonnoTrue0.020495-0.114933
88503female39.00529.1250QThirdwomanFalseQueenstownnoFalse0.0568480.684632
88711female19.00030.0000SFirstwomanFalseSouthamptonyesTrue0.058556-0.769123
88803female28.01223.4500SThirdwomanFalseSouthamptonnoFalse0.045771-0.114933
88911male26.00030.0000CFirstmanTrueCherbourgyesTrue0.058556-0.260308
89003male32.0007.7500QThirdmanTrueQueenstownnoTrue0.0151270.175818